By Institute of Medicine, Food and Nutrition Board, Board on Health Promotion and Disease Prevention, Farm-to-Table Process Risk Assessment, Committee on the Review of the USDA E. coli O157:H7
File from the Committee at the evaluate of the USDA E-coli 0157:H7, Farm to desk procedure possibility review and illness Prevention. Softcover.
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Additional resources for Escherichia coli O157:H7 in ground beef: review of a draft risk assessment
Coli O157:H7 contamination in beef from the time it is ground until the time it is ready to be prepared for consumption. Various factors can affect how much E. coli O157:H7 growth, if any, occurs during this time: the storage time and temperature (including effects of freezing), the fat content, the strain of E. coli O157:H7, and the packaging. To model growth of E. coli O157:H7 in ground beef, three assumptions are made. • All areas of a product are at the same temperature. • All E. coli O157:H7 strains exhibit the same growth characteristics in any ground-beef product.
Coli O157:H7 infection has the potential to induce serious illness and cause death, it is not possible to conduct dose-response testing on human subjects; the model must rely on information accumulated from other sources to make the calculations in this module. To determine how many cases of E. coli O157:H7-induced illness occur in the United States annually, information was gathered from the 1996– 1999 Emerging Infections Program, Foodborne Disease Active Surveillance Network (FoodNet). Dose-response estimates are calculated by using the FoodNet data and input from the exposure-assessment modules regarding amounts of E.
PRODUCTION MODULE 33 herd prevalence (because cull cows are shipped on particular days rather than on average days). If that argument is accepted, a distribution based on pooling of multiple sampling visits, such as that portrayed in Figure 3-3, should not be used. The committee recommends that a decision be made as to whether distribution of within-herd prevalence by herdday or by herd is more appropriate for the model and that only studies relevant to the chosen metric be used. Estimates Are Biased by Use of Within-Herd Prevalence Data Only in Positive Herds Use of data only on herds detected as positive (in contrast with herds actually positive), especially if samples per herd are relatively small, results in a biased estimate of within-herd prevalence and its distribution.